Gait waveform feature extracting method and individual identification system

ABSTRACT

A gait waveform feature extracting method and an individual identification system extract features of the gait waveform. A one-step waveform corresponding to one step of a walking movement is specified using, as an index, a peak amplitude corresponding to a state where substantially a whole bottom surface of one foot is in contact with the ground and a toe of the other foot is just after leaving the ground among the electric field displacement formed on the human body in accordance with the human body&#39;s walking movements. Based on the specified one-step waveform, the features of the one-step waveform are extracted, so that the peak amplitude appears without influence from electric-charge interference between the right and left legs. Accordingly, the one-step waveform reflects the actual one step of the walking movement, and therefore, the features of the one-step waveform can be precisely extracted.

FIELD OF THE INVENTION

The present invention relates to a gait waveform feature extractingmethod and an individual identification system, and is suitably appliedto an individual identification system for identifying individuals basedon electric field displacement formed on a human body in accordance withhis/her walking movements.

DISCUSSION OF THE BACKGROUND

Recently, there are individual identification systems for identifyingindividuals by extracting biometric features peculiar to a human bodysuch as an iris of an eye, a finger print, or a handprint and byperforming a prescribed matching process based on the extracted results.

In addition, in recent years, human walking movements itself have beenfocused on as one biometric feature peculiar to a human body Forexample, there is an individual identification system for identifyingindividuals based on extracted results obtained by extracting biometricsfeatures through a frequency analysis on acoustic oscillation (sound)generated by walking movements.

This individual identification system requires a microphone (acoustic toelectric transducer) to be placed on a human body to detect a walkingcycle using, as an index for one step, a subset of electric signalsobtained by collecting acoustic oscillation energy during his/herwalking movements through the microphone. The subset of signalsrepresenting acoustic oscillation at a moment of a foot landinggenerated by an impact caused when one foot part lands on the ground isgathered and then a system is used to extract features of a gaitwaveform peculiar to the body based on the detected results. (See, USPatent Publication US2002/0107649A1 (e.g., FIGS. 6 and 7), the entirecontents of which being incorporated herein by reference.)

However, in this individual identification system, the acousticoscillation at the point of landing varies according to the part of thebody on which the microphone is placed. In addition, the electrical partrepresenting the oscillation at landing cannot be accurately specifieddue to major influences from acoustic and electrical noise around themicrophone, and accordingly it is difficult to precisely extractfeatures of the gait waveform.

SUMMARY OF THE INVENTION

The present invention is made in consideration of the above points andproposes a gait waveform feature extracting method and an individualidentification system for precisely extracting features of a gaitwaveform.

To solve the above-described and other problems, the present inventionspecifies a one-step waveform from electric field displacement formed ona human body in accordance with his/her two-leg-walking movements,using, as an index, a peak amplitude within a prescribed frequency bandrepresenting a state where a whole bottom surface of one foot is incontact with the ground and a toe of the other foot is just afterleaving the ground. This one-step waveform represents one step of thetwo-leg-walking movements, and then extracts the features of theone-step waveform.

With this invention, the peak amplitude of the output signal appearswithout influence from electric-charge interference between the rightand left legs. This peak amplitude is then used as an index regardlessof walking pattern differences due to differences between the right andleft legs or differences among individuals, so that one step can be nearaccurately specified.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 A block diagram showing the construction an individualidentification system which is applied of the present invention

FIG. 2 A schematic diagram showing the construction of an electric fielddisplacement detection part according to the present invention.

FIG. 3 A schematic diagram showing a spectrograph example.

FIG. 4 A schematic diagram showing a walking pattern.

FIG. 5 A schematic diagram showing an equivalent circuit of the electricfield displacement detection part.

FIG. 6 A timing chart explaining the output signal as a function ofwalking movements.

FIG. 7 A flow chart showing a procedure of a gait waveform registrationprocess according to the present invention.

FIG. 8 A schematic diagram illustrating how to identify and divide aone-step waveform.

FIG. 9 A schematic diagram showing integrated values in subdivisionsegments.

FIG. 10 A schematic diagram showing distribution examples ofregistration waveform feature parameter groups.

FIG. 11 A flow chart showing a procedure of a gait waveform matchingprocess.

FIG. 12 A schematic diagram explaining the calculation of a Mahalanobisdistance.

DETAILED DESCRIPTION OF THE INVENTION

Hereinafter, one embodiment of the present invention will be describedin detail with reference to the attached drawings.

Construction of Individual Certification System

In FIG. 1, reference numeral 1 shows an individual identification systemas a whole to which the present invention is applied. The systemincludes an electric field displacement detection part 2, an analysispart 3 connected (via wired or wireless mechanisms) to the electricfield displacement detection part 2, and an input part 4, a memory 5,and an output part 6 connected to the analysis part 3. The system isformed so that the entire system, or at least the electric fielddisplacement detection part 2, can directly contact with skin of a humanbody such as an arm and an ankle. Moreover, the electric fielddisplacement detection part 2 may be configured as a wearable device andbe configured as a wristwatch, a ring, jewelry or other wearableconfiguration such that the detection part 2 substantially contacts theuser's outer skin.

The individual identification system 1 is configured to identifyindividuals by detecting changes such as a change of an electrostaticcapacity formed between bottom surfaces of feet and a road (or other)surface in accordance with human walking movements. Changes in electricfield displacement formed on a human body are caused by changes, forexample, in movement of electric charges between bottom surfaces of feetand a road surface. The system 1 produces an electric signal (referredto as a gait waveform signal hereafter) S1 at the electric fielddisplacement detection part 2 that is indicative of the change inelectric field displacement. The detected gait waveform signal S1 isthen analyzed at the analysis part 3.

Here, walking movements in this embodiment means movements where aperson walks on an almost flat road (or other) surface without beingparticularly sensitive about the walking speed. It should be noted that,in the electric field displacement formed on a human body in accordancewith his/her walking movements, the frequency is so low and thewavelength is so long that an extremely wide range of electrostaticfield is dominant.

As shown in FIG. 2, the electric field displacement detection part 2includes a field effect transistor (referred to as a FET hereafter) 10,a main electrode 11 connected to the gate of the FET 10, a dielectric 12inserted between the main electrode 11 and outer skin of the detectiontarget's body OS, and an amplifier 13 of which one terminal is connectedto the source of the FET 10 and the other terminal is connected to thedrain of the FET 10 at the same time.

The outer skin of the body, OS, is connected to the middle point MPbetween the drain of the FET 10 and the other terminal of the amplifier13 via the dielectric 12. And the amplifier 13 is configured to operatewith an internal electrical power source or an electrical power sourcesupplied from outside. Even an active/passive device can be used, wherethe FET is powered by an external source when the active/passive deviceis brought near, but not necessarily in contact with, the externalsource.

Accordingly, when electric field displacement at an extremely lowfrequency band formed on the detection target's body in accordance withhis/her walking movements is transmitted on the outer skin of the bodyOS, the electric field displacement detection part 2 detects a lowcurrent corresponding to the electric field displacement via the FET 10by the potential generated between the dielectric 12 and the mainelectrode 11, then sends the detected low current to the analysis part 3(FIG. 1) as a gait waveform signal S1 via the amplifier 13.

In this case, in the electric field displacement detection part 2, theelectric field displacement formed on the body is at an extremely lowfrequency band, therefore, the electric field displacement formed inaccordance with his/her walking movements can be precisely detected withalmost no influence from noise, such as a hum noise, created by strayfields and other sources.

Furthermore, the electric field displacement detection part 2 cansensitively detect the electric field displacement formed in accordancewith the detection target's walking movements by directly connecting thedielectric 12 with his/her outer skin OS, and still further, by formingthe dielectric 12 with materials having a high dielectric constant suchas soft polyvinyl chloride, so that the electric field displacementformed in accordance with the walking movements can be detectedsensitively.

As described above, the electric field displacement measuring instrument2 can detect electric field displacement without irradiating searchbeams such as microwave on the body of a detection target person. Inaddition to the above mentioned construction, the electric fielddisplacement detection part 2 is configured to be grounded (or earthed)to the outer skin of a body OS via a guard electrode 14 that surroundsboth the FET 10 and the main electrode 11 while being grounded to theouter skin of the body OS via the middle point MP. This grounding maytake place in of several ways, including a separate conductive member(such as a lead or a tab) that interconnects the guard electrode to theOS, or by forming a bottom portion of the guard electrode 14 topartially wrap around an underside of the dielectric 12 so as to contactthe OS. Alternatively, the guard electrode need not physically contactthe OS.

With this construction and with the guard electrode 14, the electricfield displacement detection part 2 can avoid detecting frequencyelements (noises) other than electric field displacement formed inaccordance with the human walking movements as much as possible.

The gait waveform signal S1 detected by the electric field displacementdetection part 2 as described above, as shown in FIG. 3, at the lowfrequency band on and under 20 Hz, shows a strong and weak pattern ofelectric field strength just like a voiceprint as time goes by.

Here, a corresponding relationship between changes of electric fieldstrength (electric potential) and a pattern of human walking movements(referred to as a walking pattern hereafter) will be discussed. First,as a basic assumption, a human walking pattern and a mechanism ofgenerating electric field strength will be explained.

In a human walking pattern of a right leg, as shown in FIG. 4, roughlythree kinds of processes are sequentially repeated: a taking off processwhich starts just after the right heel leaves the ground (which meansleaving the road surface, and likewise hereafter) and ends right afterthe right toe leaves the ground (FIG. 4A); a kicking process whichstarts just after the right toe leaves the ground and ends right beforethe right heel lands on the ground (FIG. 4B); and a landing processwhich starts just after the right heel lands on the ground and ends whenthe whole bottom surface of the right foot becomes in contact with theground (referred to as a completed landing hereafter) (FIG. 4C).

On the other hand, in a human walking pattern of the left leg, roughlythree kinds of processes are sequentially repeated in the same manner asthe right leg, however, the start timing of each process is differentfrom the right leg. A “contacting the floor” process starts in themiddle of the right leg's taking off process (shown by an arrow in FIG.4A), and a taking off process starts in the middle of the right leg'slanding process (shown by an arrow in FIG. 4C).

As described above, in human walking movements, each process of rightand left legs is alternatively repeated to make him/her go forward,where the left leg's taking off process is near-half-cycle behind theright leg's landing process, and the left leg's landing process isnear-half-cycle behind the right leg's taking off process.

Next, the mechanism of generating electric field strength (electricpotential) will be explained.

By the following expression, an electrostatic capacity, where C is theelectrostatic capacity, ε is the dielectric constant, S is the area ofelectrodes, and d is the distance between the electrodes,

EXPRESSION 1

C=ε·S/d  (1)can be expressed.

Therefore, in human walking movements, the electrostatic capacity Cbecomes larger as the contact area S of the bottom surfaces of feet incontact with the ground becomes larger, and on the contrary, theelectrostatic capacity C becomes smaller as the bottom surfaces of feetleave the ground since an air layer with a smaller dielectric constantis formed corresponding to the distance d between the road surface andthe bottom surfaces of feet representing the separated area S.

In other words, each change to a separated area (or contact area) ofbottom surfaces of feet in contact with a road surface, each change ofthe distance between the road surface and the bottom surfaces of feet,and the exchange of electric charges between the road surface and thebottom surfaces of feet (electric-charging interaction) are closelyinvolved in electric field strength of electric field displacementformed in accordance with human walking movements.

Here, by the following expression, the amount of electric charges, whereQ is the amount of electric charges and V is the voltage,

EXPRESSION 2

Q=C·V  (2)can be expressed.

Here, in human walking movements, the amount of electric charge Qchanges very slightly, therefore, it can be assumed that the amount ofelectric charges Q is constant, which means that the change of theelectrostatic capacity C and the change of the voltage V are inverselyrelated.

Therefore, in human walking movements, when the electrostatic capacity Csuddenly decreases as a bottom surface of a foot rapidly leaves the roadsurface during the taking off process (FIG. 4A), a dielectric breakdownof air is exceeded to cause electric-discharging due to a suddenincrease of the voltage V between the bottom surface of foot and theroad surface.

As described above, in human walking movements, the voltage V increasesas the bottom surfaces of feet leave the road surface, and at the sametime, electric-discharging occurs between the bottom surfaces of feetand the road surface in accordance with a sudden decrease of theelectrostatic capacity C.

Accordingly, in the mechanism of generating electric field strengthchanging in accordance with human walking movements, the voltage Vcaused by the potential difference between the bottom surfaces of feetof a body and the road surface is not the only factor, but theelectrostatic capacity C is also a factor closely involved.

Taking the above-mentioned basic assumption into consideration, thecorresponding relationship between a human walking pattern and changesof electric field strength generated in accordance with human walkingmovements will be described based on a model where an electrostaticcapacity C is formed between bottom surfaces of feet of a body and aroad surface.

As such a model, an equivalent circuit is used assuming that a variablecapacitor is formed between the road surface and each of four parts ofthe bottom surfaces of feet, where the four parts are formed byseparating the bottom surface of left foot into two: one covering thearea starting at the middle of the long direction of the left foot andending at the left heel; and the other covering the area starting at themiddle of the long direction of the left foot and ending at the lefttoe, and by separating the bottom surface of the right foot into two inthe same way as the left foot: one covering the right heel; and theother covering the right toe.

As shown in FIG. 5, an equivalent circuit 20 has the almost same circuitdesign as the electric field displacement detection part 2 (FIG. 2),however, it is assumed that, at the gate of the FET 10 in the electricfield displacement detection part 2, as replacements of the mainelectrode 11 and the dielectric 12, there are four variable capacitorsconnected to the gate of the FET 10: a capacitor CL1 formed between theleft heel and the road surface; a capacitor CL2 formed between the lefttoe and the road surface; a capacitor CR1 formed between the right heeland the road surface; and a capacitor CR2 formed between the right toeand the road surface.

It should be noted that the part inside of the dotted line has theconstruction of the electric field displacement detection part 2 issimplified for the sake of convenience.

Using the equivalent circuit 20, the corresponding relationship among ahuman walking pattern, a change of the amount of electric charges of abody Q, a change of the electrostatic capacity C formed between bottomsurfaces of feet and a road surface, and the change of the voltage Vbetween the bottom surfaces of feet and the road surface (which meansthe change of the electric field strength of a gait detection signalS1), is described in FIG. 6 as a timing chart, and the description ofthe timing chart is summarized in table 1.

It should be noted that, for the sake of convenience, leakage resistanceof the road surface is ignored, the amount of electric charges of a bodyQ is regarded as constant since the changes thereof are very small, andit is assumed that the heel and toe clearly separately leave the groundwhen the bottom surfaces of the foot leave the ground.

In addition, note that a human walking pattern covers the movementsstarting at the state where the right leg is behind the person's centerof mass, and the right heel is at just before leaving the ground (theleft-end state shown in FIG. 4A), through the kicking process (FIG. 4B)of the right leg, and ending at the state where the left toe is at justafter leaving the ground (the right-end state shown in FIG. 4C).

TABLE 1 Walking Movements Changes of Electrostatic Capacity ElectricPotential/ Electric Charges [1] A right heel leaves Electric-charges byseparation occur Electric charges are Q. the ground.(electric-discharging) between the bottom surface of the right foot andthe road surface at the moment when the right heel leaves the ground.The right heel is The distance between the right heel and graduallyleaving the the road surface is getting longer, and the road surface.capacitor C_(R1) can be regarded as 0 when the distance becomes longenough. Therefore, the capacity of the body changes from C_(R1) + C_(R2)to C_(R2). [2] The left heel lands on The capacity of the body becomesC_(L1) + Electric potential the ground. C_(R2) as the left heel makesthe area being changes to Q/(C_(L1) + in contact with the ground larger.C_(R2)). [3] The whole bottom The capacity of the body becomes C_(L1) +Electric potential surface of the left foot is C_(L2) + C_(R2) as thebottom surface of the left changes to Q/(C_(L1) + in contact with thefoot makes the area being in contact with C_(L2) +C_(R2)). ground. theground larger. [4] The right toe leaves Electric-charges by separationoccur Electric potential is the ground. (electric-discharging) betweenthe bottom Q/(C_(L1) + C_(L2) +C_(R2)). surface of the right foot andthe road surface at the moment when the right toe leaves the ground. Theright toe is gradually The distance between the right heel and Electricpotential leaving the road surface. the road surface is getting longer,and the changes to Q/(C_(L1) + capacitor C_(R2) can be regarded as 0when C_(L2)). the distance becomes long enough. Therefore, the capacityof the body changes to C_(L1) + C_(L2). [5] The right leg kicksElectric-charges by separation occur forward, and the left heel(electric-discharging) between the bottom leaves the ground beforesurface of the left foot and the road the right heel lands on surface atthe moment when the left heel the ground. leaves the ground. The leftheel is gradually The distance between the left heel and the Electricpotential leaving the road surface. road surface is getting longer, andthe changes to Q/(C_(L1) + capacitor C_(L1) can be regarded as 0 whenC_(L2)) from Q/C_(L2). the distance becomes long enough. Therefore, thecapacity of the body changes from C_(L1) + C_(L2) to C_(L2). [6] Theright heel lands The capacity of the body becomes C_(L2) + Electricpotential on the ground. C_(R1). changes to Q/(C_(L2) + C_(R1)). [7] Thewhole bottom The capacity of the body becomes C_(L2) + Electricpotential is surface of the right foot C_(R1) + C_(R2). Q/(C_(L2) +C_(R1) + C_(R2)). is in contact with the ground. [8] The left toe leavesElectric-charges by separation occur the ground. (electric-discharging)between the bottom surface of the left foot and the road surface at themoment when the left toe leaves the ground. The left toe is graduallyThe distance between the left toe and the Electric potential leaving theroad surface. road surface is getting longer, and the changes fromQ/C_(L2) + capacitor C_(L2) can be regarded as 0 when C_(R1) + C_(R2))to Q/(C_(R1) + the distance becomes long enough. C_(R2)). Therefore, thecapacity of the body changes from C_(L2) + C_(R1) + C_(R2) to C_(R1) +C_(R2).

As described in table 1, the gait waveform shown in FIG. 6 is the resultof the changes of the electrostatic capacity and the electric potentialchanging in accordance with each of the states of the walking patternsrepeating alternatively with a near-half-cycle difference (items [1] to[8] of walking movements in table 1), and is corresponding to the strongand weak pattern of electric field strength shown in FIG. 3.

Therefore, the gait waveform appears with a unique pattern while theelectrostatic capacity and the electric potential change correspondingto biometrics differences such as differences between right and leftlegs, or among individuals, and differences of walking patterns such asa walking path.

By the way, in the gait waveform shown in FIG. 6, sharp peak amplitudes(®-Σ at measured waveform in FIG. 6) are seen at times whenelectric-charges by separation occur (electric-discharging)corresponding to each of the following states: just after the right heelleaves the ground ([1] in FIG. 6, motion of the right leg); just afterthe right toe leaves the ground ([4] in FIG. 6, motion of the rightleg); just after the left heel leaves the ground ([5] in FIG. 6, motionof the left leg); and just after the left toe leaves the ground ([7] inFIG. 6, motion of the left leg).

Such peak amplitudes can be classified into two sets: the first one isthe peak seen just after a heel leaves the ground; and the second one isthe peak seen just after a toe leaves the ground. As for the first peak,when it is just after the right (or left) heel completely leaves theground (shown in FIG. 4, inside of the dotted line), the left (or right)foot is airborne and not in contact with the ground.

Therefore, the peak amplitude corresponding to the state where it isjust after the right or left heel leaves the ground (® and ™ in FIG. 6)appears relatively apparently by electric-charge separation(electric-discharging), however, is limited by the electric-chargeinterference between the right and left legs, and as described above, adifference of amplitude is also seen as the state of the electric-chargeinterference between the right and the left legs changes correspondingto the differences of the walking patterns between the right and leftlegs.

On the contrary, as for the second peak, when it is just after the right(or left) toe completely leaves the ground (shown in FIG. 4, inside ofthe dotted line), due to the characteristic of a walking pattern, theleft (or right) leg is in a state of a completed landing regardless ofthe differences of walking patterns.

Therefore, peak amplitude corresponding to the state where it is justafter the right or left toe leaves the ground (© and Σ in FIG. 6),appears at about 8 Hz±2 Hz bandwidth with almost no variation as themaximum peak amplitude among the gait waveform since there is noelectric-charge interference between the right and left legs. In thiscase, as for gait waveforms measured for a plurality of test walkers,the peak amplitude at 8 Hz±2 Hz bandwidth (referred to as an 8 Hz peakhereafter) appears in the same peak-amplitude-frequency characteristic.

As described above, the gait waveform of the gait detection signal S1(FIG. 1) appears with a unique pattern, while the electrostatic capacityand the electric potential change with differences in walking patterns.However, a distinct 8 Hz peak appears at an almost constant intervalcorresponding to the repetition where the right or left toe leaves theground, regardless of the differences of walking patterns.

By the way, the analysis part 3 (FIG. 1), by executing a gait waveformregistration process at the operation of prescribed registration startoperation through the input part 4 (e.g., external interface port, orperipheral device), extracts features of a registration waveform usingthe above-mentioned 8 Hz peak as an index among the gait waveformsignals (referred to as a registration gait waveform signal hereafter)S1 provided from the electric field displacement detection part 2corresponding to, for example, the walking movements of a person wearingthe individual identification system 1 (referred to as a registrationperson hereafter) during the operation, and then stores the extractedresults into the memory 5 as a registration waveform feature data D1.

At this stage, the analysis part 3, by executing a gait waveformmatching process at the operation of prescribed matching start operationthrough the input part 4, at first, extracts features of a matchingtarget waveform using the above-mentioned 8 Hz peak as an index amongthe gait waveform signals (referred to as a matching gait waveformsignal hereafter) S2 provided from the electric field displacementdetection part 2 corresponding to, for example, the walking movements ofthe body wearing the individual identification system 1 (referred to asa matching target person hereafter) during the operation.

Next, the analysis part 3 identifies if the person is a registrationperson or not by matching the features of the matching target waveformand the features of the registration waveforms of the registrationperson feature data D1 stored in the memory 5, and then generates andtransmits an identification result data D2 representing the identifiedresults.

The output part 6 is configured to generate an output signalcorresponding to the acceptance status of the output target circuit(system), and generates an infrared signal S3 by transforming theidentification result data D2 provided from the analysis part 3 by, forexample, photoelectric transfer, and outputs the signal to outside.

In the memory 5, average interval between peaks information whichrepresents an average interval between an 8 Hz peak and the next 8 Hzpeak (referred to as an interval between peaks hereafter) is stored asinformation which the analysis part 3 uses at the gait waveformregistration process or the gait waveform matching process.

In practice, the analysis part 3 is configured to execute the gaitwaveform registration process and the gait waveform matching process inaccordance with an analysis program previously stored in the memory 5 bya control part composed of parts not shown in figures such as a CPU, aROM and a RAM, but which are contained in the analysis part 3. Theanalysis part 3 may be hosted on a personal computer, handheld computer,PDA or other processor-based device that includes a memory, or access(locally or remotely) to a memory. A connection between the electricfield displacement detection part 2 and the analysis part 3 may be via awired connection or a wireless connection. The wired connection mayinclude a physical connector (e.g., USB 2, or other peripheralconnector) that enables the electric field displacement detection part 2to forward collected signals to the analysis part 3 (e.g., store andforward). Alternatively, the electric field displacement detection part2 may include a wireless transmitter, such as IEEE 802.11a, 802.11b,802.11g, or Bluetooth transmitter for example. The analysis part 3includes a compatible receiver for receiving the signal S1 transmitted(via a direct connection or a wireless connection) from the electricfield displacement detection part 2.

First, the gait waveform registration process will be explained indetail using the following flowchart.

Gait Waveform Registration Process

As shown in FIG. 7, the analysis part 3 starts at a start step of aroutine RT1, goes on to a step SP1 to generate registration gaitwaveform data by conducting an A/D conversion process on theregistration gait waveform signal S1 (FIG. 2) per prescribed unit oftime provided from the electric field displacement detection part 2,then goes on to a next step SP2.

At the step SP2, the analysis part 3 removes frequency elements at orover 40 Hz (e.g., via a digital low pass filter) which are not analysistargets from the registration gait waveform data, then generatesregistration gait waveform data at around 8 Hz band as shown in FIG. 8(referred to as a registration gait waveform at 8 Hz band hereafter) by,for example, a quadrature conversion process such as Fast FourierTransform (FFT), then goes on to a next step SP3.

At the step SP3, the analysis part 3 removes the waveforms representingmovements other than normal walking movements such as walking movementsat the start or at the end, or stop movements, from the registrationgait waveform at 8 Hz band generated at the step SP2, then goes on to anext step SP4.

In practice, the analysis part 3 is configured to detect an 8 Hz peakappearing in the registration gait waveform at 8 Hz band, and to removethe waveforms which are beyond the prescribed allowable range of the 8Hz interval between peaks comparing to the average interval betweenpeaks information previously stored in the memory S (FIG. 1) from thedetected 8 Hz interval between peaks.

Here, the analysis part 3 is configured to adequately preserve theregistration gait waveform at 8 Hz band representing the normal walkingmovements by removing the waveforms representing movements other thanthe normal walking movements using, as an index, the 8 Hz peak appearingat an almost constant interval regardless of the differences of thewalking patterns.

At the step SP4, the analysis part 3 is configured to specify the lengthbetween the center of a standard 8 Hz peak and the middle of 8 Hz peaksin front and behind the standard 8 Hz peak as a unit representing theactual one step of the walking movements (referred to as a one-step unithereafter) SU. The analysis part 3 also generates a plurality steps of aone-step registration waveform by dividing the registration gaitwaveform at 8 Hz band obtained at the step SP3 by the one-step sectionunit SU, then goes on to a next step SP5.

Here, the analysis part 3 is configured to adequately generate aone-step registration waveform representing actual one step of walkingmovements by dividing the registration gait waveform at 8 Hz band byone-step unit SU specified using an 8 Hz peak as an index.

At the step SP5, the analysis part 3 judges whether the number of stepsof the one-step registration waveform taken at the step SP4 has reachedthe prescribed number (referred to as prescribed number of stepshereafter) or not.

A negative result means that the number of steps of one-stepregistration waveform taken at the step SP4 is less than the prescribednumber of steps, and the analysis part 3 returns to the step SP1 andrepeats the above-mentioned process.

On the contrary, an affirmative result at the step SP5 means that thenumber of steps of one-step registration waveform taken at the step SP4has reached the prescribed number of steps, and the analysis part 3 goeson to a next step SP6.

At the step SP6, the analysis part 3 divides each of the prescribednumber of steps of the one-step registration waveform taken at the stepSP4 into subdivision segments at almost even intervals, for example,twenty-one subdivision segments CSU1 to CSU21 respectively.

Then, the analysis part 3 extracts integrated values obtained byintegrating amplitude values (values of electric field strength) on thesubdivision segments CSU1 to CSU21 as features of the one-stepregistration waveform, then goes on to a next step SP7.

Here, FIG. 9 is a graph showing the integrated values obtained when, forexample, each of five steps of one-step registration waveform is dividedinto twenty-one subdivision segments CSU1 to CSU21 respectively. Itshould be noted that the integrated values are normalized.

One line in FIG. 9 represents one step of one-step registrationwaveform, as connecting a plurality of integrated values obtained as aresult of integrating the one-step registration waveform over each ofthe twenty-one subdivision segments CSU1 to CSU21, therefore, representsa detailed approximate form of the original one-step registrationwaveform.

Accordingly, each group of the integrated values of each of thesubdivision segments CSU1 to CSU21 forming one line represent thefeatures of the one-step registration waveform peculiar to each of theregistration persons. Furthermore, the five groups of integrated valueson each of the subdivision section each comprised of subdivisionsegments CSU1 to CSU21 are the groups of features of each part of theone-step registration waveform, therefore, represent an area certifyingthe registration person for each part.

At the step SP7, the analysis part 3 identifies each of the ten groupsof integrated values on the subdivision sections each comprised ofsubdivision segments CSU1 to CSU21 as a group of registration waveformfeature parameter groups GR1 to GR21 respectively, then goes on to anext step SP8.

At the step SP8, the analysis part 3, for example as shown in FIG. 10,generates registration waveform feature distribution data representing adistribution state of the registration waveform feature parameter groupsGR1 and GR2 on twenty-one-dimensional space, and generates registrationwaveform feature distribution data of the registration waveform featureparameter groups GR3 to GR21 in the same manner, then goes on to a nextstep SP9.

It should be noted that FIG. 10 shows a distribution state ontwo-dimensional space for the sake of convenience. In addition, theanalysis part 3 shows a distribution state on twenty-one-dimensionalspace, however, in practice, is configured to show a distribution stateon an x-dimensional space where x is the number of times dividing aone-step registration waveform at the step SP6.

At the step SP9, the analysis part 3 generates registration waveformfeature data D2 by converting each of the registration waveform featuredistribution data corresponding to each of the registration waveformfeature parameter groups GR1 to GR21 generated at the step SP8 into aprescribed data storing format, and stores the data into the memory 5(FIG. 1), then goes on to a next step SP10 to end the gait waveformregistration process.

As described above, the analysis part 3 is configured to take out, atprescribed registration start operation, a one-step registrationwaveform using an 8 Hz peak as an index, and integrates amplitude valueson a plurality of subdivision segments of the one-step registrationwaveform so that integrated values representing features of the one-stepregistration waveform peculiar to the registration person can beaccurately extracted.

Gait Waveform Matching Process

Next, a gait waveform matching process at the analysis part 3 will bedescribed in detail using the following flowchart.

As shown in FIG. 11, the analysis part 3 starts at a start step of aroutine RT2, goes on to a step SP21 to execute each process of the stepsSP1, SP2, and SP3 of the above-mentioned routine RT1 (FIG. 1) on amatching gait wave signal S2 per prescribed unit of time provided fromthe electric field displacement detection part 2 so that a matching gaitwaveform at 8 Hz band representing a waveform removed of waveformsrepresenting movements other than normal walking movements can begenerated, then goes on to a next step SP22.

At the step SP22, in the same manner as the step SP4, the analysis part3 generates one step of a one-step matching waveform by dividing thematching gait waveform at 8 Hz band obtained at the step SP21 by theone-step section unit SU (FIG. 8), then goes on to a next step SP23.

At the step SP23, the analysis part 3 divides the one-step matchingwaveform taken at the step SP22 into the same number of subdivisionsegments as the number of times dividing at the step SP6, for example,into twenty-one subdivision segments CSU1 to CSU21 (FIG. 8).

Then, the analysis part 3 extracts integrated values obtained byintegrating electric field strength (electric potential) on thesubdivision segments CSU1 to CSU21 as features of the one-step matchingwaveform, then goes onto a next step SP24.

At the step SP24, the analysis part 3, as shown in FIG. 11, calculatesthe Mahalanobis distance between the integrated value CP of thesubdivision segment CSU1 extracted at the step SP24 and the barycentricposition of, for example, the distribution DS1 to DS3 of the threeregistration waveform feature parameter groups GR1s corresponding to thesubdivision segment CSU1 of three registration persons. The quantity rinr ²=(x−m _(x))′C _(x) ⁻¹(x−m _(x))  (3)is called the Mahalanobis distance from the feature vector x to the meanvector m_(x), where C_(x) is the covariance matrix for x.

Furthermore, the analysis part 3 calculates the Mahalanobis distance forthe integrated values of the subdivision segments CSU2 to CSU21 in thesame manner, then goes on to a next step SP25. At the step SP25, theanalysis part 3, for each of the three registration persons, totals theMahalanobis distances calculated on the three registration persons andfor each of the subdivision segments CSU1 to CSU21 at the step SP24, andbased on the Mahalanobis distance totaled for each of the registrationpersons, calculates an individual identification ratio representing theratio of the possibility that the matching target person can beidentified as one of the registration persons him/herself for each, thengoes on to a next step SP26.

In practice, the analysis part 3 is configured to calculate a higherindividual identification ratio as the totaled Mahalanobis distance isshorter, and on the contrary, calculate a lower individualidentification ratio as the totaled Mahalanobis distance is longer.

At the step SP26, the analysis part 3 judges whether one or more of aplurality of the individual identification ratios calculated at the stepSP25 is/are 90% or higher, or not. Alternatively, other thresholds maybe used such as 75% or greater, or 85% or greater or even any thresholdbetween 75% and 100% inclusive. A negative result means that theone-step matching waveform of the matching target person has a lowmatching ratio with the one-step registration waveforms of theregistration persons previously stored in the memory 5, in other words,the matching target person cannot be identified as one of theregistration persons him/herself identification, and accordingly, theanalysis part 3 identifies the matching target person as not one of theregistration persons, and then goes onto a next step SP29.

On the contrary, an affirmative result means that the matching targetperson is appropriate as one of the registration persons, and theanalysis part 3 goes on to a next step SP27.

At the step SP27, the analysis part 3 judges whether there exist two ormore individual identification ratios which are 90% or higher among theplurality of the individual identification ratio calculated at the stepSP25, or not. A negative result means that there exists just oneapplicant as a registration person who should be identified as thematching target person him/herself, therefore, the analysis part 3identifies the registration person being an applicant as the matchingtarget person, then goes onto a next step SP29.

On the contrary, an affirmative result means that there exist two ormore applicants as registration person who could be identified as thematching target parson him/herself, and the analysis part 3 goes on to anext step SP28 to identify the registration person having the highestand at least 90% individual identification ratio as the matching targetperson, then goes onto a next step SP29.

At the step SP29, the analysis part 3 generates identification resultdata D2 corresponding to the identified result at the step SP26, SP27 orSP28 (FIG. 1), transmits the data to the output part 6, and then goes onto a next step SP30 to end the gait waveform matching process.

When there exist two or more registration persons having the highest andat least 90% individual identification ratio, the analysis part 3 isconfigured to transmit the information as identification result data D2to the output part 6.

As described above, the analysis part 3 takes out a one-step matchingwaveform using, as an index, an 8 Hz peak using, as an index, an 8 Hzpeak appearing at an almost constant interval regardless of differencesof walking patterns, and extracts integrated values on the same numberof subdivision segments CSU1 to CSU21 (FIG. 8) as the number of timesdividing during a gait waveform process as features of the one-stepmatching waveform, then identifies whether the matching target person isone of the registration persons based on a Mahalanobis distance betweenthe distribution of the registration waveform feature parameter groupsGR1 to GR21, or not, therefore, can match the registration persons andthe matching target person by matching the corresponding part of thewaveforms of one step of the registration persons and the matchingtarget person, so that the matching target person can be preciselyidentified. Once identified, the system produces a result that may beused in any one of a number of ways. For example, once the systemrecognizes a person, the system may produce a visual or audio signal toannounce the fact that a match has been made. Alternatively, the systemmay use the recognition event to trigger a control signal for actuatinga device (e.g., a door so the person can enter).

In the above-mentioned construction, the individual identificationsystem 1 is configured to detect the change of the electrostaticcapacity formed between the road surface and the bottom surfaces of feetin accordance with human walking movements, and the relative change ofelectric field displacement formed on the human body caused by thechanges of electric charges between the road surface and the bottomsurfaces of feet as a gait waveform signal S1 or S2 by the electricfield displacement detection part 2.

In this case, as the changes of electrostatic capacity and of electriccharges spread wide at an extremely low frequency band, the individualidentification system 1 can detect the relative changes of electricfield displacement without being influenced by the position of theelectric field displacement detection part 2, and from noise such as ahum noise and a noise around the electric field displacement detectionpart 2 as a gait waveform signal S1 or S2.

At this state, the individual identification system 1 is configured tospecify a one-step waveform using, as an index, one of the gait waves ofthe gait waveform signal S1 and S2, which is a distinctive 8 Hz peak at8 Hz±2 Hz bandwidth appearing corresponding to the state where a wholebottom surface of one foot is in contact with the ground and a toe ofthe other foot is at right after leaving the ground (shown in FIG. 4,inside of the dotted line).

In this case, the individual identification system 1 can use the maximumpeak amplitude appearing without influence from the electric-chargeinterference between the right and left legs as an index, and therefore,can accurately specify a one-step waveform which exactly reflects anactual one step of walking movements even when the walking movementsappear with a unique pattern corresponding to the differences betweenthe right and left legs or differences of walking patterns amongindividuals.

Furthermore, the individual identification system 1 divides the one-stepwaveform into a plurality of subdivision segments CSU1 to CSU21 (FIG.8), and extracts integrated values obtained by integrating the amplitudevalues each on each of the divided subdivision segments CSU1 to CSU21 asfeatures of the waveform of the one-step waveform, so that a detailedapproximate form of the one-step waveform can be represented, therefore,the individual identification system 1 can precisely identifyindividuals without matching the one-step waveform itself.

With above-mentioned construction, one-step waveform corresponding toone step of walking movements can be specified using, as an index, an 8Hz peak corresponding to the state where a whole bottom surface of onefoot is in contact with the ground and a toe of the other foot is atright after leaving the ground among the electric field displacementformed on a human body in accordance with the human body's walkingmovements, and based on the specified one-step waveform, the features ofthe one-step waveform are extracted, so that the maximum peak amplitudeappearing without influenced from electric-charged interference betweenthe right and left legs can be the index and accordingly one-stepwaveform exactly reflecting the actual one step of the walking movementscan be accurately specified, therefore, the features of the one-stepwaveform can be precisely extracted.

The description above has dealt with the case where the analysis part 3analyzes the electric field displacement formed on a human body inaccordance with the human body's walking movements, however, is notlimited to this and electric field displacement formed on a human bodyin accordance with movements such as brisk walking, going up and downthe stairs, or stepping, to sum up, any two-leg-walking movements wherethe bottom surface of one foot is in contact with the ground and the toeof the other foot is at just after leaving the ground can be the targetfor the analysis.

In this case, the maximum peak amplitude of the gait waveform variescorresponding to the movement speed between the state of right (or left)leg's completed landing and the state right after the right (or left)leg's toe leaves the ground, therefore, to acquire the same effect asdescribed above, the analysis part 3 is required to use the peakamplitude appearing at the frequency band corresponding to the movementspeed between the state of a completed landing of the right (or left)leg and the state right after the toe of the right (or left) leg leavesthe ground during the two-leg-walking movements to be the detectiontarget as a replacement of the 8 Hz peak for detection.

Furthermore, the embodiment described above has dealt with the casewhere the gait of a body, to which the electric field displacementdetection part 2 is directly connected, is detected where the person isregarded as a registration person or a matching target person, however,is not limited to this and a person being around the person having theelectric field displacement detection part 2 directly connected can be aregistration person or a matching target person whose gait should bedetected.

In this case, the amplitude experiences a time series increase inaccordance with an approach, therefore the analysis part 3 can preciselydetect the gait of the registration person or the matching target personwho is at a relatively far position from the body having the electricfield displacement detection part 2 directly connected as theelectrostatic field displacement spread extremely wide on the body inaccordance with the body's walking movements if the analysis part 3previously stores the corresponding relationship between the distancebetween the individual identification system 1 and the position of thedetection target, and the peak amplitude being an 8 Hz peak.

Furthermore, the above-description has dealt with the case where theelectric field displacement detection part 2 is directly connected tothe outer skin of a body OS, however, is not limited to this and theelectric field displacement detection part 2 can be connected to variouskinds of conductors, and for example, can be installed on a mobile phoneor a pedometer, or be placed on a metal pole or a desk.

In this case, the present invention can be used not only for identifyingindividuals, but also for various kinds of purposes relating totwo-leg-walking movements, for example, for analyzing walking patternsof patients (walkers) at medical institutions, and for identifying ahuman body from bodies of animals at biometrics field.

Furthermore, the embodiment described above has dealt with the case withconstruction where the electric field detection part 2 is used as anelectric field displacement detection means as described above, however,is not limited to this, and the present invention can be configured touse, as a replacement of the electric field detection part 2, amagnetometric sensor called as a Superconducting Quantum InterferenceDevice (SQID) in which joint parts (Josephson junctions) beingsuperconducting are placed in parallel with microfabricatedsuperconduction thin films.

Furthermore, the embodiment described above has dealt with the casewhere an analysis part 3 and an electric field detection part 2 are setup as an identification means in an individual identification system 1,but is not limited to this, and each of the electric field detectionpart 2 and the analysis part 3 can be separately set up individually.

Furthermore, the embodiment described above has dealt with the casewhere a one-step waveform is divided into a plurality of subdivisionsegments CSU1 to CSU21 (FIG. 8), and the integrated values obtained bycalculating for each of the subdivision segments CSU1 to CSU21 areextracted as the features of the one-step waveform, however, is notlimited to this, and the amplitude values at the division of theplurality of the subdivision segments CSU1 to CSU21 (FIG. 8) withoutbeing integrated can be extracted as the features of the one-stepwaveform.

Furthermore, the embodiment described above has dealt with the casewhere each process at the analysis part 3 is realized by an analysisprogram, however, is not limited to this, and a part of or all of theprocesses can be realized by a hardware means such as a dedicatedintegrated circuit, such as an ASIC or a FPGA.

Furthermore, the embodiment described above has dealt with the casewhere the above-described gait waveform registration process (FIG. 7)and the gait waveform matching process (FIG. 11) are executed inaccordance with analysis programs previously stored in the memory 5,however, is not limited to this, and the gait waveform registrationprocess and the gait waveform matching process can be executed byinstalling a program storage medium for storing the analysis programs inan information processing device.

In this case, as a program storage medium for installing the analysisprograms for executing the gait waveform registration process and thegait waveform matching process in the information processing device fora executable state, for example, can be not only package media such as aflexible disk, a compact disk-read only memory (CD-ROM), and a digitalversatile disc (DVD), but also a semiconductor memory or a magnetic diskin which programs are temporally or permanently stored. In addition, asa means for storing analysis programs in such program storage medium,wired or wireless communication medium such as a local area network, theinternet, and a digital satellite broadcast can be used, and storing canbe made through various communication interfaces such as a router and amodem.

According to the present invention as described above, a one-stepwaveform corresponding to the one step of two-leg-walking movements isspecified using, as an index, peak amplitude at a prescribed frequencyband corresponding to the state where a whole bottom surface of one footis in contact with the ground and a toe of the other foot is at justafter leaving the ground among the electric field displacement formed onthe human body in accordance with his/her two-leg-walking movements, andbased on the specified one-step waveform, the features of the one-stepwaveform are extracted, so that maximum peak amplitude appearing withoutinfluenced from electric-charged interference between the right and leftlegs can be the index and accordingly one-step waveform exactlyreflecting the actual one step of the walking movements can beaccurately specified, therefore, the features of the one-step waveformcan be precisely extracted.

The present patent document is related to Japanese priority document JP2002-314920, filed in the Japanese Patent Office on Oct. 29, 2002, theentire contents of which being incorporated herein by reference.

Obviously, numerous modifications and variations of the presentinvention are possible in light of the above teachings. It is thereforeto be understood that within the scope of the appended claims, theinvention may be practiced otherwise than as specifically describedherein.

1. A gait waveform feature extracting method comprising: specifying aone-step waveform from a portion of a digital signal, said digitalsignal corresponding to an electric field displacement formed on a bodyof a subject in accordance with a two-leg-walking movement of saidsubject, said specifying including associating as an index of saidone-step waveform a peak amplitude in a predetermined frequency band,said peak amplitude corresponding to a state where approximately a wholebottom surface of a first foot of said subject is in contact with awalking surface and a toe of a second foot of said subject is just afterleaving the walking surface; extracting features of said one-stepwaveform after said one-step waveform is specified in said specifyingstep; and identifying said subject based on said features extracted bysaid extracting.
 2. The gait waveform feature extracting methodaccording to claim 1, wherein said predetermined frequency band is in aninclusive range of 6 Hz through 10 Hz.
 3. The gait waveform featureextracting method according to claim 1, further comprising: retrievingthe digital signal from memory.
 4. The gait waveform feature extractingmethod according to claim 1, wherein said identifying includes comparingsaid features of said one-step waveform against a second waveform storedin memory; and determining that said one-step waveform matches saidsecond waveform when said features of said one-step waveform are withina predetermined criteria of corresponding features of said secondwaveform.
 5. The gait waveform feature extracting method according toclaim 4, further comprising: generating a control signal; and actuatinganother device once said determining determines that the one-stepwaveform matches said second waveform.
 6. The gait waveform featureextracting method according to claim 5, wherein said actuating includesat least one of actuating a visual display, actuating an audio alarm,and opening a lock.
 7. The gait waveform feature extracting methodaccording to claim 5, wherein said determining includes calculating aMahalanobis distance from said features of said first waveform.
 8. Thegait waveform feature extracting method according to claim 1, whereinsaid extracting step includes dividing said one-step waveform by aninterval so as to create divided intervals, and extracting as thefeatures of said one-step waveform integrated values obtained byintegrating amplitude values of said divided intervals.
 9. The gaitwaveform feature extracting method according to claim 1, furthercomprising: generating said digital signal with an electric fielddisplacement detector.
 10. The gait waveform feature extracting methodaccording to claim 9, wherein said generating includes producing saiddigital signal as a wireless signal.
 11. The gait waveform featureextracting method according to claim 10, wherein said extracting isperformed in an analysis device that is separate from said electricfield displacement detector.
 12. An individual identification systemcomprising: an electric field displacement detector configured to detectan electric field displacement formed on a body of a subject inaccordance with a two-leg-walking movement of said subject and produce asignal that corresponds with the electric field displacement; and aprocessor configured to identify from said signal an individual using,as an index, a peak amplitude of said signal, in a predeterminedfrequency band, that corresponds to a state where approximately a wholebottom surface of a first foot of said subject is in contact with awalking surface and a toe of a second foot is just after leaving thewalking surface.
 13. The individual identification system according toclaim 12, wherein said predetermined frequency band is in an inclusiverange of 6 Hz through 10 Hz.
 14. The individual identification systemaccording to claim 13, further comprising: a memory configured to holdfeatures of a second waveform associated with the individual, whereinsaid processor is configured to compare said features of said one-stepwaveform against the second waveform stored in memory, and determinethat said one-step waveform matches said second waveform when saidfeatures of said one-step waveform are within a predetermined criteriaof corresponding features of said second waveform.
 15. The individualidentification system according to claim 14, wherein said electric fielddisplacement detector is configured to generate a control signal whensaid processor determines that said one-step waveform matches saidsecond waveform; and said processor is configured to actuate anotherdevice after receiving said control signal once said determining stepdetermines that the one-step waveform matches said second waveform. 16.The individual identification system according to claim 15, wherein saidanother device being at least one of a visual display, an audio alarmmechanism, and a controllable lock.
 17. The individual identificationsystem according to claim 14, wherein said processor is configured tocalculate a Mahalanobis distance from said features of said firstwaveform.
 18. The individual identification system according to claim15, wherein said electric field displacement detector includes atransmitter configured to transmit said control signal as a wirelesssignal.
 19. The individual identification system according to claim 14,wherein said electric field displacement detector is separate from saidprocessor.
 20. An individual identification system comprising: means fordetecting an electric field displacement formed on a subject inaccordance with a two-leg-walking movement of said subject to generate awaveform; and means for comparing said waveform with predeterminedwaveforms associated with different individuals so as to identify apredetermined individual based on said electric field displacement.